Artificial intelligence (AI) is the key driver of competitiveness in the modern economy. Companies that integrate AI infrastructures now will secure long-term market share. 61% of companies report that the use of AI tools has increased by 2025—a clear sign of the growing importance of AI in everyday business operations. (MaibornWolff Study on Technology Efficiency, p. 21) This guide analyzes the most important areas of application, technical requirements, and strategic steps for AI implementation.
- What is AI? Artificial intelligence refers to technologies that simulate human decision-making structures, learn from data, and solve complex problems autonomously.
- Who can benefit from using AI? AI can be used across all industries to increase efficiency—from predictive maintenance in manufacturing to automation in the service sector.
- What are the top areas of application for artificial intelligence in companies? AI can currently support almost any form of knowledge-based work, such as accounting, customer service, market research, engineering, knowledge management, autonomous control, quality control, and software development.
- How can its full potential be exploited? The full potential is realized by integrating AI into existing workflows, ensuring a clean database (data engineering), and empowering employees.
What is AI?
Artificial intelligence (AI) differs from conventional software in its adaptability. While classic algorithms operate on a rule-based basis, AI systems (machine learning) learn independently from data sets. They recognize patterns, adapt to new information, and make autonomous decisions. This ability to adapt makes AI a key technology for scalable automation and data-driven business models.
Artificial intelligence dominates the IT agenda, yet its impact is mixed. The available data suggests that deploying AI without a fundamental strategy and clearly defined goals does not automatically solve problems, but rather amplifies existing inefficiencies and drastically increases the volume of digital waste. (MaibornWolff Study on Technology Efficiency, p. 21)
Still have questions about machine learning, NLP, or the difference between strong and weak AI? Then take a look at these guides:
Using AI: Always profitable for businesses?
The ROI (return on investment) of AI projects primarily results from efficiency gains and competitive advantages. Current data from the Federal Statistical Office shows a clear adoption curve:
- Large companies: 48% are already actively using AI technologies.
- SMEs: 28% use AI in their day-to-day operations.
This adoption gap poses massive risks for latecomers: those who delay integration accumulate technological debt. A lack of infrastructure and employee expertise will later prevent rapid responses to market changes.
Early investment in training and test environments, on the other hand, secures a strategic knowledge advantage and enables iterative validation of profitable use cases ahead of the competition.
This is already paying off for some companies: 42% of respondents cite increased process efficiency as the primary benefit. (MaibornWolff Study on Technology Efficiency, p. 23)
At the same time, market research shows that the promise of AI has not yet been fulfilled everywhere: 19% of respondents currently see no tangible business value in AI. (MaibornWolff Study on Technology Efficiency, p. 23)
The conclusion is clear: Those who view AI merely as a panacea will be disappointed. Those who, on the other hand, see it as a specialized tool will reap significant benefits. (MaibornWolff Study on Technology Efficiency, p. 23)
AI is particularly relevant in data- and process-intensive industries:
- Industry & manufacturing: automotive industry, mechanical engineering, chemical industry, electronics.
- Consumer goods: manufacturers of consumer goods and fast-moving consumer goods (FMCG).
- Services & infrastructure: insurance, energy supply, and network operators.
Would you like to know whether AI is worthwhile for your company? We would be happy to advise you.
Artificial intelligence in companies: Successful use cases
Now that we have given you a brief taste of the exciting possibilities of AI in business, we would like to go into a little more detail. Below, we present more specific AI use cases that show how AI can be used in practice.
1. Communication and Knowledge Management (NLP)
Large language models (LLMs) are revolutionizing the processing of unstructured data in text form.
Our study shows that the sheer volume of AI tools poses challenges: around 47% of IT managers and specialists feel overwhelmed by the sheer number and frequency of new AI applications—making a consolidated platform strategy all the more important. (MaibornWolff Study on Technology Efficiency, p. 22)
- Customer service & help desks: Chatbots and virtual assistants provide database-driven answers in real time (internally and externally), create tickets, and guide users through troubleshooting.
- Document analysis: AI automatically extracts information from operating manuals or contracts, checks it for compliance (e.g., style, completeness), and keeps data sheets up to date.
- Customer analytics: By linking internal data with external trends, AI systems segment user groups more precisely and enable personalized communication.
2. Software Engineering & Testing
AI-powered coding assistants (co-pilots) massively accelerate the software development lifecycle (SDLC):
- Requirements analysis: Automated creation of specifications and user stories.
- Code creation: Support through autocomplete and refactoring suggestions.
- Quality assurance: Automated generation of test cases and optimization of CI/CD pipelines for error-free deployments.
The impact is measurable: Generative AI produces content and code at a pace that is virtually impossible to keep up with manually. (MaibornWolff Study on Technology Efficiency, p. 22)
AI agents, in particular, are accelerating modernization: By using AI agents, existing software landscapes and complex domains can be analyzed and modernized much more quickly. (MaibornWolff Study on Technology Efficiency, p. 23)
Is your current software holding you back? We offer the solution.
3. Generative design and industrial control
In physical value creation, AI combines creative design processes with efficient manufacturing:
- Generative AI in design (shape shifting): Technologies such as computer vision and synthetic image generation enable the flexible modification of 3D models and prototypes in product development and medical technology.
- Smart manufacturing: AI systems process sensor data in real time (edge computing) to control machines autonomously. This enables demand-driven utilization, reduces energy costs, and optimizes predictive maintenance.
However, market research urges caution—the so-called Jevons paradox applies here as well: Since AI drastically reduces the cost and time required to create code and content, we are not producing less code in less time, but simply producing significantly more code overall. Without proper governance, there is a risk of an inflation of digital assets. (MaibornWolff Study on Technology Efficiency, p. 22)
And if you want to discover even more exciting examples of artificial intelligence applications in business, take a look at these 33 additional use cases:
Success factors for implementing artificial intelligence in companies
The greatest opportunities for AI lie in the automation of complex tasks to increase efficiency and competitiveness. However, the operational added value does not come from the technology alone, but from its intelligent integration into three core strategic areas: databases, workflows, and human resources.
Data quality and availability (data engineering)
High-quality data is essential for powerful AI. According to the principle of "garbage in, garbage out", the quality of results correlates directly with the quality of the data. Companies must break down data silos and make information available across systems in order to train or use accurate AI models.
This is confirmed by real-world experience: 56% report a noticeable loss of time due to a lack of integration and the resulting system disconnects. Without end-to-end data integration, even the best AI remains ineffective. (MaibornWolff Study on Technology Efficiency, p. 10)
Process integration
AI must not be an isolated solution. It must be deeply integrated into existing workflows in order to transfer contextual information from one process step to the next. This is the only way to create continuous automation chains.
After all, AI cannot fill the gaps in processes: inefficiencies in the software landscape arise, for example, when processes are not fully automated, interfaces are incomplete, and data must be processed manually multiple times. (MaibornWolff Study on Technology Efficiency, p. 5)
This leads directly to a crucial warning: When AI is applied to inefficient processes, it does not result in greater efficiency, but merely in doing the wrong thing faster. (MaibornWolff Study on Technology Efficiency, p. 21)
Empowering employees
The technical expertise of employees is essential for training and validating AI results. Companies must create spaces for experimentation (sandbox environments) so that teams can identify specific use cases from practice.
However, one key problem remains: 64% of IT managers and specialists report that employees are rarely or never involved in technology decisions. (MaibornWolff Study on Technology Efficiency, p. 15)
This has implications for acceptance: 48% of respondents agree that technological complexity sometimes causes uncertainty or even anxiety among colleagues. (MaibornWolff Study on Technology Efficiency, p. 16)
Therefore, the success of a software implementation is not measured by the number of features, but by the adoption rate and employee satisfaction. (MaibornWolff Study on Technology Efficiency, p. 12)
Challenges: Compliance and ethics
In addition to technical expertise, the implementation of AI systems also requires consideration of complex legal and ethical frameworks. Proactively addressing these hurdles is crucial for long-term project success and acceptance within the company.
Legal hurdles
Companies in the EU operate in a highly regulated environment. The EU AI Act (in force since August 1, 2024) classifies AI applications according to risk groups and stipulates specific transparency and documentation requirements. Compliance with these regulations is mandatory in order to avoid fines and reputational damage.
Data protection and privacy
The protection of sensitive company and customer data (GDPR) is a key requirement. However, security concerns can be effectively addressed through modern architecture models:
- Data protection-compliant integration: The use of private cloud instances or on-premise hosting (local hardware) prevents data leakage to public models.
- Access control: Role-based access systems ensure that AI models only access authorized data sources. This allows security requirements to be scaled individually without sacrificing the benefits of AI.`
We help you collect, understand and efficiently use your data.
AI and ethics
Ethical guidelines form the basis for trustworthy AI. The following dimensions must be taken into account during development:
- Accessibility: AI interfaces must be designed to be inclusive and enable use regardless of physical limitations.
- Employee well-being: The focus is on relieving routine tasks to promote mental health and personal development, not on surveillance.
- Inclusion and fairness: Training data must be checked for bias to technically rule out discrimination based on origin, gender, or religion.
- Responsibility and sustainability: The use of resource-saving models (green AI) and a transparent energy balance are essential for a sustainable corporate strategy.
Ethical concerns also extend to the code: 59% of respondents fear that digital waste—specifically unused technical features, dead code, and redundant artifacts—will increase in the future due to AI. This is because technical debt grows rapidly: this trend carries the risk that technical debt will no longer be reduced in a controlled manner, but will instead be automatically multiplied. (MaibornWolff Study on Technology Efficiency, p. 22)
Launch into the future with MaibornWolff
MaibornWolff supports companies from strategic planning to the technical implementation of AI solutions. Our approach is technology-agnostic and strictly oriented toward your company's maturity level:
Technology should always be used where it adds value—not for its own sake. [...] It is important to avoid technological over-engineering and to build the IT systems of the future in a lean manner.
Source: MaibornWolff study “Technology Efficiency,” p. 32
- Consulting & Strategy: We analyze your existing infrastructure and identify high-value use cases – regardless of whether you are just starting out or have already scaled your first AI projects.
- End-to-End Implementation: Our teams take care of the technical implementation, from data integration to deployment in your IT landscape.
- Enablement & Training: Through targeted training, we empower your employees to use AI tools productively and independently.
We focus on measurable business value and pragmatic solutions that are tailored precisely to your needs.
The key to new competitiveness lies not in adding more tools, but in the ability to eliminate the unnecessary. (MaibornWolff Study on Technology Efficiency, p. 6)
We are happy to support you on your journey to using AI.
The future of AI in business
The market is moving away from exclusive knowledge monopolies toward broad availability of technology (“commoditization”). This results in two key strategic advantages for companies:
- Open-source parity: Freely available models are now reaching the performance level of commercial solutions. This enables companies to achieve technological independence without vendor lock-in.
- Efficiency and edge AI: Modern AI models are so resource-efficient that they can be run on standard hardware (laptops, mobile devices). This reduces infrastructure costs and enables data protection-compliant applications directly on the end device.
This opens up new opportunities for investment: By freeing up budgets from the management of legacy systems, companies can generate the investment capacity needed for innovation and a faster time-to-market. (MaibornWolff Study on Technology Efficiency, p. 6)
FAQs
Will AI replace human workers?
No, AI will not replace human workers, but rather support them. It automates routine tasks, allowing humans to focus on creative and complex activities. With the right training, employees and companies alike can benefit from AI.
The data confirms this: 49% of respondents report that measures to reduce IT complexity have led to increased employee acceptance of the software. (MaibornWolff Study on Technology Efficiency, p. 30)
How much does it cost to implement AI solutions?
The cost of implementing AI solutions varies greatly and depends on the complexity, scope and type of solution. Small projects with existing infrastructure can be relatively inexpensive, while larger, customised solutions require higher investments. However, open-source models and scalable cloud solutions offer flexible and budget-friendly options, even for smaller companies.
Is AI only suitable for large companies?
No, small and medium-sized enterprises can also benefit from AI. Many open-source models and scalable AI solutions are now available for smaller budgets and companies.
How does AI affect employees?
AI can take some of the pressure off employees by taking over routine tasks and creating more space for creative, value-adding activities. However, it is important to involve employees early on in the introduction process and offer them training to ensure a smooth transition.
The reality is that 54% of respondents confirm they have to adapt their work processes to the systems in use because these systems do not meet their existing needs. AI solutions must be designed around people—not the other way around. (MaibornWolff Study on Technology Efficiency, p. 15)
Kyrill Schmid is Lead AI Engineer in the Data and AI division at MaibornWolff. The machine learning expert, who holds a doctorate, specialises in identifying, developing and harnessing the potential of artificial intelligence at the enterprise level. He guides and supports organisations in developing innovative AI solutions such as agent applications and RAG systems.